The invention relates generally to systems for processing image data, and more particularly, to systems for segmenting of image data.
Segmentation of image data includes labeling of image voxels according to the tissue type of each voxel. Segmentation of images in a 2D pixel dataset and in a 3D voxel dataset is needed for analysis of anatomical structures. The difference in the intensity of the image voxels is used to determine the boundary or edges of the image. However, several unknown variables such as tissue type, correlation with neighboring voxels, image non-uniformity, partial volume artifacts and detector noise may create uncertainty in the segmentation process.
Different methods are known for performing segmentation. In one class of methods, a contour-based algorithm is applied to the image data. In a contour-based method, a curve is generated based on internal forces, such as, curvature, and external forces, such as, image gradients. The curve delineates the boundaries of anatomical structures.
In another class of methods, a region-based algorithm is applied to the image data. Some region-based methods identify clusters of pixels/voxels that have some similarity. The image is divided into regions, for each region similarity among pixels/voxels is analyzed. If the similarity level is below a threshold, the region is divided into smaller regions. Neighboring regions with similar features are then merged into a larger region. This process is performed iteratively until there is no more splitting or merging. Other region-based methods use statistical modeling of each tissue class, combined with morphological operations such as smoothing and connectivity. These region-based methods use a Bayesian probabilistic framework. Additionally, extensions of the Bayesian probabilistic framework include mechanisms for providing spatial coherence, such as Markov random fields (MRFs). MRF models can be based on an assumption of piecewise homogeneity of tissues.
Segmentation also may be performed by computing an anatomically correct co-ordinate transformation (registration) between the image and an already segmented atlas image.
These known algorithms often do not provide satisfactory results in clinical settings. Thus, human intervention is often required to extract clinically meaningful results. This results in a tedious process with an increased likelihood of error (e.g., human error). Further, the process can be very time consuming.